'''simple docstring'''




import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__UpperCAmelCase					={
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[Any]:
    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts							)
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config							)
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights							)

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Dict:
    if args.student_type == "roberta":
        __lowerCamelCase            =    False
    elif args.student_type == "gpt2":
        __lowerCamelCase            =    False






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    if args.student_type == "roberta":
        __lowerCamelCase            =    False






def __lowerCAmelCase					(				)      ->       Tuple:
    __lowerCamelCase            =    argparse.ArgumentParser(description='''Training'''							)
    parser.add_argument('''--force'''							,    action='''store_true'''							,    help='''Overwrite dump_path if it already exists.'''							)

    parser.add_argument(
        '''--dump_path'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''The output directory (log, checkpoints, parameters, etc.)'''							)
    parser.add_argument(
        '''--data_file'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''							,    )

    parser.add_argument(
        '''--student_type'''							,    type=UpperCamelCase__							,    choices=['''distilbert''', '''roberta''', '''gpt2''']							,    required=UpperCamelCase__							,    help='''The student type (DistilBERT, RoBERTa).'''							,    )
    parser.add_argument('''--student_config'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''Path to the student configuration.'''							)
    parser.add_argument(
        '''--student_pretrained_weights'''							,    default=UpperCamelCase__							,    type=UpperCamelCase__							,    help='''Load student initialization checkpoint.'''							)

    parser.add_argument(
        '''--teacher_type'''							,    choices=['''bert''', '''roberta''', '''gpt2''']							,    required=UpperCamelCase__							,    help='''Teacher type (BERT, RoBERTa).'''							)
    parser.add_argument('''--teacher_name'''							,    type=UpperCamelCase__							,    required=UpperCamelCase__							,    help='''The teacher model.'''							)

    parser.add_argument('''--temperature'''							,    default=2.0							,    type=UpperCamelCase__							,    help='''Temperature for the softmax temperature.'''							)
    parser.add_argument(
        '''--alpha_ce'''							,    default=0.5							,    type=UpperCamelCase__							,    help='''Linear weight for the distillation loss. Must be >=0.'''							)
    parser.add_argument(
        '''--alpha_mlm'''							,    default=0.0							,    type=UpperCamelCase__							,    help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''							,    )
    parser.add_argument('''--alpha_clm'''							,    default=0.5							,    type=UpperCamelCase__							,    help='''Linear weight for the CLM loss. Must be >=0.'''							)
    parser.add_argument('''--alpha_mse'''							,    default=0.0							,    type=UpperCamelCase__							,    help='''Linear weight of the MSE loss. Must be >=0.'''							)
    parser.add_argument(
        '''--alpha_cos'''							,    default=0.0							,    type=UpperCamelCase__							,    help='''Linear weight of the cosine embedding loss. Must be >=0.'''							)

    parser.add_argument(
        '''--mlm'''							,    action='''store_true'''							,    help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''							)
    parser.add_argument(
        '''--mlm_mask_prop'''							,    default=0.1_5							,    type=UpperCamelCase__							,    help='''Proportion of tokens for which we need to make a prediction.'''							,    )
    parser.add_argument('''--word_mask'''							,    default=0.8							,    type=UpperCamelCase__							,    help='''Proportion of tokens to mask out.'''							)
    parser.add_argument('''--word_keep'''							,    default=0.1							,    type=UpperCamelCase__							,    help='''Proportion of tokens to keep.'''							)
    parser.add_argument('''--word_rand'''							,    default=0.1							,    type=UpperCamelCase__							,    help='''Proportion of tokens to randomly replace.'''							)
    parser.add_argument(
        '''--mlm_smoothing'''							,    default=0.7							,    type=UpperCamelCase__							,    help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''							,    )
    parser.add_argument('''--token_counts'''							,    type=UpperCamelCase__							,    help='''The token counts in the data_file for MLM.'''							)

    parser.add_argument(
        '''--restrict_ce_to_mask'''							,    action='''store_true'''							,    help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''							,    )
    parser.add_argument(
        '''--freeze_pos_embs'''							,    action='''store_true'''							,    help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''							,    )
    parser.add_argument(
        '''--freeze_token_type_embds'''							,    action='''store_true'''							,    help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''							,    )

    parser.add_argument('''--n_epoch'''							,    type=UpperCamelCase__							,    default=3							,    help='''Number of pass on the whole dataset.'''							)
    parser.add_argument('''--batch_size'''							,    type=UpperCamelCase__							,    default=5							,    help='''Batch size (for each process).'''							)
    parser.add_argument(
        '''--group_by_size'''							,    action='''store_false'''							,    help='''If true, group sequences that have similar length into the same batch. Default is true.'''							,    )

    parser.add_argument(
        '''--gradient_accumulation_steps'''							,    type=UpperCamelCase__							,    default=50							,    help='''Gradient accumulation for larger training batches.'''							,    )
    parser.add_argument('''--warmup_prop'''							,    default=0.0_5							,    type=UpperCamelCase__							,    help='''Linear warmup proportion.'''							)
    parser.add_argument('''--weight_decay'''							,    default=0.0							,    type=UpperCamelCase__							,    help='''Weight decay if we apply some.'''							)
    parser.add_argument('''--learning_rate'''							,    default=5E-4							,    type=UpperCamelCase__							,    help='''The initial learning rate for Adam.'''							)
    parser.add_argument('''--adam_epsilon'''							,    default=1E-6							,    type=UpperCamelCase__							,    help='''Epsilon for Adam optimizer.'''							)
    parser.add_argument('''--max_grad_norm'''							,    default=5.0							,    type=UpperCamelCase__							,    help='''Max gradient norm.'''							)
    parser.add_argument('''--initializer_range'''							,    default=0.0_2							,    type=UpperCamelCase__							,    help='''Random initialization range.'''							)

    parser.add_argument(
        '''--fp16'''							,    action='''store_true'''							,    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''							,    )
    parser.add_argument(
        '''--fp16_opt_level'''							,    type=UpperCamelCase__							,    default='''O1'''							,    help=(
            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
            '''See details at https://nvidia.github.io/apex/amp.html'''
        )							,    )
    parser.add_argument('''--n_gpu'''							,    type=UpperCamelCase__							,    default=1							,    help='''Number of GPUs in the node.'''							)
    parser.add_argument('''--local_rank'''							,    type=UpperCamelCase__							,    default=-1							,    help='''Distributed training - Local rank'''							)
    parser.add_argument('''--seed'''							,    type=UpperCamelCase__							,    default=56							,    help='''Random seed'''							)

    parser.add_argument('''--log_interval'''							,    type=UpperCamelCase__							,    default=5_00							,    help='''Tensorboard logging interval.'''							)
    parser.add_argument('''--checkpoint_interval'''							,    type=UpperCamelCase__							,    default=40_00							,    help='''Checkpoint interval.'''							)
    __lowerCamelCase            =    parser.parse_args()
    sanity_checks(UpperCamelCase__							)

    # ARGS #
    init_gpu_params(UpperCamelCase__							)
    set_seed(UpperCamelCase__							)
    if args.is_master:
        if os.path.exists(args.dump_path							):
            if not args.force:
                raise ValueError(
                    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                    ''' itUse `--force` if you want to overwrite it'''							)
            else:
                shutil.rmtree(args.dump_path							)

        if not os.path.exists(args.dump_path							):
            os.makedirs(args.dump_path							)
        logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""							)

        # SAVE PARAMS #
        logger.info(f"""Param: {args}"""							)
        with open(os.path.join(args.dump_path							,    '''parameters.json'''							)							,    '''w'''							) as f:
            json.dump(vars(UpperCamelCase__							)							,    UpperCamelCase__							,    indent=4							)
        git_log(args.dump_path							)

    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    MODEL_CLASSES[args.student_type]
    __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    __lowerCamelCase            =    teacher_tokenizer_class.from_pretrained(args.teacher_name							)
    __lowerCamelCase            =    {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        __lowerCamelCase            =    tokenizer.all_special_tokens.index(UpperCamelCase__							)
        __lowerCamelCase            =    tokenizer.all_special_ids[idx]
    logger.info(f"""Special tokens {special_tok_ids}"""							)
    __lowerCamelCase            =    special_tok_ids
    __lowerCamelCase            =    tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(f"""Loading data from {args.data_file}"""							)
    with open(args.data_file							,    '''rb'''							) as fp:
        __lowerCamelCase            =    pickle.load(UpperCamelCase__							)

    if args.mlm:
        logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""							)
        with open(args.token_counts							,    '''rb'''							) as fp:
            __lowerCamelCase            =    pickle.load(UpperCamelCase__							)

        __lowerCamelCase            =    np.maximum(UpperCamelCase__							,    1							) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            __lowerCamelCase            =    0.0  # do not predict special tokens
        __lowerCamelCase            =    torch.from_numpy(UpperCamelCase__							)
    else:
        __lowerCamelCase            =    None

    __lowerCamelCase            =    LmSeqsDataset(params=UpperCamelCase__							,    data=UpperCamelCase__							)
    logger.info('''Data loader created.'''							)

    # STUDENT #
    logger.info(f"""Loading student config from {args.student_config}"""							)
    __lowerCamelCase            =    student_config_class.from_pretrained(args.student_config							)
    __lowerCamelCase            =    True

    if args.student_pretrained_weights is not None:
        logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""							)
        __lowerCamelCase            =    student_model_class.from_pretrained(args.student_pretrained_weights							,    config=UpperCamelCase__							)
    else:
        __lowerCamelCase            =    student_model_class(UpperCamelCase__							)

    if args.n_gpu > 0:
        student.to(f"""cuda:{args.local_rank}"""							)
    logger.info('''Student loaded.'''							)

    # TEACHER #
    __lowerCamelCase            =    teacher_model_class.from_pretrained(args.teacher_name							,    output_hidden_states=UpperCamelCase__							)
    if args.n_gpu > 0:
        teacher.to(f"""cuda:{args.local_rank}"""							)
    logger.info(f"""Teacher loaded from {args.teacher_name}."""							)

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(UpperCamelCase__							,    UpperCamelCase__							)
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(UpperCamelCase__							,    UpperCamelCase__							)

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0							) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    __lowerCamelCase            =    Distiller(
        params=UpperCamelCase__							,    dataset=UpperCamelCase__							,    token_probs=UpperCamelCase__							,    student=UpperCamelCase__							,    teacher=UpperCamelCase__							)
    distiller.train()
    logger.info('''Let\'s go get some drinks.'''							)


if __name__ == "__main__":
  main()

'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
